Double Markov random fields and Bayesian image segmentation
Citation:
Dina Melas and Simon P. Wilson `Double Markov random fields and Bayesian image segmentation? in IEEE Transactions on Signal Processing, 50, (2), 2002, pp 357 - 365Download Item:
Abstract:
Markov random fields are used extensively in modelbased
approaches to image segmentation and, under the Bayesian
paradigm, are implemented through Markov chain Monte Carlo
(MCMC) methods. In this paper,we describe a class of such models
(the double Markov random field) for images composed of several
textures, which we consider to be the natural hierarchical model
for such a task.We show how several of the Bayesian approaches in
the literature can be viewed as modifications of this model, made in
order to make MCMC implementation possible. From a simulation
study, conclusions are made concerning the performance of these
modified models.
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http://people.tcd.ie/swilsonDescription:
PUBLISHED
Author: WILSON, SIMON PAUL
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IEEEType of material:
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IEEE Transactions on Signal Processing50
2
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